Soil biochemistry and microbial activity in vineyards under conventional and organic management at Northeast Brazil.

The São Francisco Submedium Valley is located at the Brazilian semiarid region and is an important center for irrigated fruit growing. This region is responsible for 97% of the national exportation of table grapes, including seedless grapes. Based on the fact that orgThe São Francisco Submedium Valley is located at the Brazilian semiarid region and is an important center for irrigated fruit growing. This region is responsible for 97% of the national exportation of table grapes, including seedless grapes. Based on the fact that organic fertilization can improve soil quality, we compared the effects of conventional and organic soil management on microbial activity and mycorrhization of seedless grape crops. We measured glomerospores number, most probable number (MPN) of propagules, richness of arbuscular mycorrhizal fungi (AMF) species, AMF root colonization, EE-BRSP production, carbon microbial biomass (C-MB), microbial respiration, fluorescein diacetate hydrolytic activity (FDA) and metabolic coefficient (qCO2). The organic management led to an increase in all variables with the exception of EE-BRSP and qCO2. Mycorrhizal colonization increased from 4.7% in conventional crops to 15.9% in organic crops. Spore number ranged from 4.1 to 12.4 per 50 g-1 soil in both management systems. The most probable number of AMF propagules increased from 79 cm-3 soil in the conventional system to 110 cm-3 soil in the organic system. Microbial carbon, CO2 emission, and FDA activity were increased by 100 to 200% in the organic crop. Thirteen species of AMF were identified, the majority in the organic cultivation system. Acaulospora excavata, Entrophospora infrequens, Glomus sp.3 and Scutellospora sp. were found only in the organically managed crop. S. gregaria was found only in the conventional crop. Organically managed vineyards increased mycorrhization and general soil microbial activity

Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment

The number of microbiome-related studies has notably increased the availability of data on human microbiome composition and function. These studies provide the essential material to deeply explore host-microbiome associations and their relation to the development and progression of various complex diseases. Improved data-analytical tools are needed to exploit all information from these biological datasets, taking into account the peculiarities of microbiome data, i.e., compositional, heterogeneous and sparse nature of these datasets. The possibility of predicting host-phenotypes based on taxonomy-informed feature selection to establish an association between microbiome and predict disease states is beneficial for personalized medicine. In this regard, machine learning (ML) provides new insights into the development of models that can be used to predict outputs, such as classification and prediction in microbiology, infer host phenotypes to predict diseases and use microbial communities to stratify patients by their characterization of state-specific microbial signatures. Here we review the state-of-the-art ML methods and respective software applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on the application of ML in microbiome studies related to association and clinical use for diagnostics, prognostics, and therapeutics. Although the data presented here is more related to the bacterial community, many algorithms could be applied in general, regardless of the feature type. This literature and software review covering this broad topic is aligned with the scoping review methodology. The manual identification of data sources has been complemented with: (1) automated publication search through digital libraries of the three major publishers using natural language processing (NLP) Toolkit, and (2) an automated identification of relevant software repositories on GitHub and ranking of the related research papers relying on learning to rank approach.This study was supported by COST Action CA18131 “Statistical and machine learning techniques in human microbiome studies”. Estonian Research Council grant PRG548 (JT). Spanish State Research Agency Juan de la Cierva Grant IJC2019-042188-I (LM-Z). EO was founded and OA was supported by Estonian Research Council grant PUT 1371 and EMBO Installation grant 3573. AG was supported by Statutory Research project of the Department of Computer Networks and Systems

Effects of farming system and simulated drought on biodiversity, food webs and ecosystem functions in the DOK trial

Organic agriculture promotes overall biodiversity in arable fields, with well-documented positive effects on plant and pollinator diversity and abundance. Responses of soil-living decomposers, aboveground herbivores and predators to organic farming are less uniform and not equally well understood. The DOK trial offers ideal conditions to assess the long-term effects of organic compared to conventional farming practices on these above- and belowground invertebrate communities. Organic treatments in the DOK trial have a pronounced effect on abundances, diversity and species composition across taxonomic borders. Application of farmyard manure promotes nematode and earthworm numbers, whereas mineral fertilizers detrimentally affected potworm and fly larvae numbers. Aboveground predators are more abundant under organic agriculture and herbivores show an opposite response. However, effects go beyond simple numeric responses as organic agriculture alters the species composition of local communities significantly

Severe drought and conventional farming affect detritivore feeding activity and its vertical distribution

11 Pág.Soil invertebrates are key to decomposition, a central ecosystem process related to soil health. In many temperate areas climate change will decrease soil water content, which strongly modulates biological activity. However, data are lacking on how shifts in rainfall patterns affect soil biota and the ecosystem processes they provide. Here, we used the bait-lamina test to experimentally assess how a severe drought event influenced detritivore feeding activity, during a wheat growing season, in soils under long-term organic or conventional farming. Additionally, biotic and abiotic soil parameters were measured. Feeding activity was reduced under extreme drought and conventional management, although no climate-management synergies were found. Vertical migrations of Collembola and Oribatida partially explained the unexpectedly higher bait consumption at shallower depths in response to drought. Exploratory mixed-effects longitudinal random forests (a novel machine learning technique) were used to explore whether the relative abundances of meso‑, microfauna and microbes of the decomposer food web, or abiotic soil parameters, affected the feeding activity of detritivores. The model including meso‑ and microfauna selected four Nematoda taxa and explained higher variance than the model with only microbiota, indicating that detritivore feeding is closely associated with nematodes but not with microbes. Additionally, the model combining fauna and microbiota explained less variance than the faunal model, suggesting that microbe-fauna synergies barely affected detritivore feeding. Moreover, soil water and mineral nitrogen contents were found to strongly determine detritivore feeding, in a positive and negative way, respectively. Hence, our results suggest that severe drought and conventional farming impair the feeding activity of soil detritivores and thus, probably, decomposition and nutrient mineralization in soils. Furthermore, machine learning algorithms arise as a powerful technique to explore the identity of potential key drivers relating biodiversity to ecosystem functioning.This work was financed by the BiodivERsA COFUND (2015–2016 call), in concert with the following national funders: the Swiss National Science Foundation (SNSF), the German Research Foundation (DFG), the Swedish Research Council (Formas), the Estonian Research Council (ETAG), and the Spanish Ministry of Sciences and Innovation (MICINN, ref.: PCIN-2016–045), which also funded the FPI grant of the first author PGC (ref.: PRE2020–095020). The DOK trial is funded through the Swiss Federal Office of Agriculture (FOAG).Peer reviewe

Impact of polyols on Oral microbiome of Estonian schoolchildren

BackgroundOral microbiome has significant impact on both oral and general health. Polyols have been promoted as sugar substitutes in prevention of oral diseases. We aimed to reveal the effect of candies containing erythritol, xylitol or control (sorbitol) on salivary microbiome.MethodsNinety children (11.30.6years) consumed candies during 3years. Microbial communities were profiled using Illumina HiSeq 2000 sequencing and real-time PCR.ResultsThe dominant phyla in saliva were Firmicutes (39.1%), Proteobacteria (26.1%), Bacteroidetes (14.7%), Actinobacteria (12%) and Fusobacteria (6%). The microbiome of erythritol group significantly differed from that of the other groups. Both erythritol and xylitol reduced the number of observed bacterial phylotypes in comparison to the control group. The relative abundance of the genera Veillonella, Streptococcus and Fusobacterium were higher while that of Bergeyella lower after erythritol intervention when comparing with control. The lowest prevalence of caries-related mutans streptococci corresponded with the lowest clinical caries markers in the erythritol group.ConclusionsDaily consumption of erythritol, xylitol or control candies has a specific influence on the salivary microbiome composition in schoolchildren. Erythritol is associated with the lowest prevalence of caries-related mutans streptococci and the lowest levels of clinical caries experience.Trial registration p id=Par5 ClinicalTrials.gov Identifier NCT01062633

Wetlands for wastewater treatment and subsequent recycling of treated effluent : a review

Due to water scarcity challenges around the world, it is essential to think about non-conventional water resources to address the increased demand in clean freshwater. Environmental and public health problems may result from insufficient provision of sanitation and wastewater disposal facilities. Because of this, wastewater treatment and recycling methods will be vital to provide sufficient freshwater in the coming decades, since water resources are limited and more than 70% of water are consumed for irrigation purposes. Therefore, the application of treated wastewater for agricultural irrigation has much potential, especially when incorporating the reuse of nutrients like nitrogen and phosphorous, which are essential for plant production. Among the current treatment technologies applied in urban wastewater reuse for irrigation, wetlands were concluded to be the one of the most suitable ones in terms of pollutant removal and have advantages due to both low maintenance costs and required energy. Wetland behavior and efficiency concerning wastewater treatment is mainly linked to macrophyte composition, substrate, hydrology, surface loading rate, influent feeding mode, microorganism availability, and temperature. Constructed wetlands are very effective in removing organics and suspended solids, whereas the removal of nitrogen is relatively low, but could be improved by using a combination of various types of constructed wetlands meeting the irrigation reuse standards. The removal of phosphorus is usually low, unless special media with high sorption capacity are used. Pathogen removal from wetland effluent to meet irrigation reuse standards is a challenge unless supplementary lagoons or hybrid wetland systems are used

The EU Horizon 2020 project GRACE : integrated oil spill response actions and environmental effects

This article introduces the EU Horizon 2020 research project GRACE (Integrated oil spill response actions and environmental effects), which focuses on a holistic approach towards investigating and understanding the hazardous impact of oil spills and the environmental impacts and benefits of a suite of marine oil spill response technologies in the cold climate and ice-infested areas of the North Atlantic and the Baltic Sea. The response methods considered include mechanical collection in water and below ice, in situ burning, use of chemical dispersants, natural biodegradation, and combinations of these. The impacts of naturally and chemically dispersed oil, residues resulting from in situ burning, and non-collected oil on fish, invertebrates (e.g. mussels, crustaceans) and macro-algae are assessed by using highly sensitive biomarker methods, and specific methods for the rapid detection of the effects of oil pollution on biota are developed. By observing, monitoring and predicting oil movements in the sea through the use of novel online sensors on vessels, fixed platforms including gliders and the so-called SmartBuoys together with real-time data transfer into operational systems that help to improve the information on the location of the oil spill, situational awareness of oil spill response can be improved. Methods and findings of the project are integrated into a strategic net environmental benefit analysis tool (environment and oil spill response, EOS) for oil spill response strategy decision making in cold climates and ice-infested areas